Visualización de la Información

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El presente tema expone la representación visual mediantes gráficos, nube de palabras y otros elementos. Se abordarán los puntos:

• ¿Cuál método visual utilizar?
• Visualización clásica.
• ggplot.
• Plotly.
• sunburstR
• highcharter
• Otras librerías en R….

Al finalizar la sesión podrá conocer los principales paquetes - librerías de visualización de la información en R.

Formas de visualizar

Existen muchas, muchíiiiiiiisimas formas para visualizar la información. ¿Qué deberíamos elegir? Esto depende de lo que queramos hacer.

• Barras verticales / horizontales.
• Histogramas.
• Lineas.
• Áreas.
• Disperción.
• Circular.
• Anillos o donas.
• Proyección solar.
• Bloques.
• Radar.
• Mapas.
• Nube de palabras.
• etc…

Y la lista podría seguir…

Al analizar datos nos hacemos la pregunta: ¿qué debería utlizar para visualr esta información?

Acá les presento una guía resumida:

¿Y para visual el texto que utilizamos?

Trataremos de cubrir esta pregunta más adelante, sin embargo adentarse en el mundo de la visualización es descubrir que existe una gama mucho más amplia, versatil e interesante a la hora de visualizar nuestra data.

Clásico.

lattice

ggplot2

ggchart

Plotly

La biblioteca de gráficos R de Plotly crea gráficos interactivos con calidad de publicación.

Ejemplos de cómo hacer gráficos de líneas, gráficos de dispersión, gráficos de áreas, gráficos de barras, barras de error, gráficos de caja, histogramas, mapas de calor, subtramas, gráficos de ejes múltiples y gráficos 3D.

Las principales opciones disponibles en el Plotly son :

• https://plotly.com/r/

Pero empecemos por los gráficos básicos:

• https://plotly.com/r/basic-charts/

Lineas

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:flextable':
## 
##     style

## The following object is masked from 'package:formattable':
## 
##     style

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

x <- c(1:100)
random_y <- rnorm(100, mean = 0)
data <- data.frame(x, random_y)

fig <- plot_ly(data, x = ~x, y = ~random_y, type = 'scatter', mode = 'lines')

fig

month <- c('January', 'February', 'March', 'April', 'May', 'June', 'July',
         'August', 'September', 'October', 'November', 'December')
high_2000 <- c(32.5, 37.6, 49.9, 53.0, 69.1, 75.4, 76.5, 76.6, 70.7, 60.6, 45.1, 29.3)
low_2000 <- c(13.8, 22.3, 32.5, 37.2, 49.9, 56.1, 57.7, 58.3, 51.2, 42.8, 31.6, 15.9)
high_2007 <- c(36.5, 26.6, 43.6, 52.3, 71.5, 81.4, 80.5, 82.2, 76.0, 67.3, 46.1, 35.0)
low_2007 <- c(23.6, 14.0, 27.0, 36.8, 47.6, 57.7, 58.9, 61.2, 53.3, 48.5, 31.0, 23.6)
high_2014 <- c(28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9)
low_2014 <- c(12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1)

data <- data.frame(month, high_2000, low_2000, high_2007, low_2007, high_2014, low_2014)

#The default order will be alphabetized unless specified as below:
data$month <- factor(data$month, levels = data[["month"]])

fig <- plot_ly(data, x = ~month, y = ~high_2014, name = 'High 2014', type = 'scatter', mode = 'lines',
        line = list(color = 'rgb(205, 12, 24)', width = 4)) 
fig <- fig %>% add_trace(y = ~low_2014, name = 'Low 2014', line = list(color = 'rgb(22, 96, 167)', width = 4)) 
fig <- fig %>% add_trace(y = ~high_2007, name = 'High 2007', line = list(color = 'rgb(205, 12, 24)', width = 4, dash = 'dash')) 
fig <- fig %>% add_trace(y = ~low_2007, name = 'Low 2007', line = list(color = 'rgb(22, 96, 167)', width = 4, dash = 'dash')) 
fig <- fig %>% add_trace(y = ~high_2000, name = 'High 2000', line = list(color = 'rgb(205, 12, 24)', width = 4, dash = 'dot')) 
fig <- fig %>% add_trace(y = ~low_2000, name = 'Low 2000', line = list(color = 'rgb(22, 96, 167)', width = 4, dash = 'dot')) 
fig <- fig %>% layout(title = "Average High and Low Temperatures in New York",
         xaxis = list(title = "Months"),
         yaxis = list (title = "Temperature (degrees F)"))

fig

Puntos

library(plotly)

fig <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, color = ~Species)

fig

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

Barras

library(plotly)

fig <- plot_ly(
  x = c("giraffes", "orangutans", "monkeys"),
  y = c(20, 14, 23),
  name = "SF Zoo",
  type = "bar"
)

fig

Animals <- c("giraffes", "orangutans", "monkeys")
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
data <- data.frame(Animals, SF_Zoo, LA_Zoo)

fig <- plot_ly(data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo')
fig <- fig %>% add_trace(y = ~LA_Zoo, name = 'LA Zoo')
fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')

fig

x <- c('Feature A', 'Feature B', 'Feature C', 'Feature D', 'Feature E')
y <- c(20, 14, 23, 25, 22)
data <- data.frame(x, y)

fig <- plot_ly(data, x = ~x, y = ~y, type = 'bar',
        marker = list(color = c('rgba(204,204,204,1)', 'rgba(222,45,38,0.8)',
                                'rgba(204,204,204,1)', 'rgba(204,204,204,1)',
                                'rgba(204,204,204,1)')))
fig <- fig %>% layout(title = "Least Used Features",
         xaxis = list(title = ""),
         yaxis = list(title = ""))

fig

Burbujas

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers',
        marker = list(size = ~Gap, opacity = 0.5))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

data_2007 <- data[which(data$year == 2007),]
data_2007 <- data_2007[order(data_2007$continent, data_2007$country),]
slope <- 2.666051223553066e-05
data_2007$size <- sqrt(data_2007$pop * slope)
colors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951')

fig <- plot_ly(data_2007, x = ~gdpPercap, y = ~lifeExp, color = ~continent, size = ~size, colors = colors,
        type = 'scatter', mode = 'markers', sizes = c(min(data_2007$size), max(data_2007$size)),
        marker = list(symbol = 'circle', sizemode = 'diameter',
                      line = list(width = 2, color = '#FFFFFF')),
        text = ~paste('Country:', country, '<br>Life Expectancy:', lifeExp, '<br>GDP:', gdpPercap,
                      '<br>Pop.:', pop))
fig <- fig %>% layout(title = 'Life Expectancy v. Per Capita GDP, 2007',
         xaxis = list(title = 'GDP per capita (2000 dollars)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(2.003297660701705, 5.191505530708712),
                      type = 'log',
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwidth = 2),
         yaxis = list(title = 'Life Expectancy (years)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(36.12621671352166, 91.72921793264332),
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwith = 2),
         paper_bgcolor = 'rgb(243, 243, 243)',
         plot_bgcolor = 'rgb(243, 243, 243)')

fig

Barras horizontales

y <- c('giraffes', 'orangutans', 'monkeys')
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
data <- data.frame(y, SF_Zoo, LA_Zoo)

fig <- plot_ly(data, x = ~SF_Zoo, y = ~y, type = 'bar', orientation = 'h', name = 'SF Zoo',
        marker = list(color = 'rgba(246, 78, 139, 0.6)',
                      line = list(color = 'rgba(246, 78, 139, 1.0)',
                                  width = 3)))
fig <- fig %>% add_trace(x = ~LA_Zoo, name = 'LA Zoo',
            marker = list(color = 'rgba(58, 71, 80, 0.6)',
                          line = list(color = 'rgba(58, 71, 80, 1.0)',
                                      width = 3)))
fig <- fig %>% layout(barmode = 'stack',
         xaxis = list(title = ""),
         yaxis = list(title =""))

fig

Sunburst (proyección solar)

fig <- plot_ly(
  labels = c("Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"),
  parents = c("", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"),
  values = c(10, 14, 12, 10, 2, 6, 6, 4, 4),
  type = 'sunburst'
)

fig

d <- data.frame(
    ids = c(
    "North America", "Europe", "Australia", "North America - Football", "Soccer",
    "North America - Rugby", "Europe - Football", "Rugby",
    "Europe - American Football","Australia - Football", "Association",
    "Australian Rules", "Autstralia - American Football", "Australia - Rugby",
    "Rugby League", "Rugby Union"
  ),
  labels = c(
    "North<br>America", "Europe", "Australia", "Football", "Soccer", "Rugby",
    "Football", "Rugby", "American<br>Football", "Football", "Association",
    "Australian<br>Rules", "American<br>Football", "Rugby", "Rugby<br>League",
    "Rugby<br>Union"
  ),
  parents = c(
    "", "", "", "North America", "North America", "North America", "Europe",
    "Europe", "Europe","Australia", "Australia - Football", "Australia - Football",
    "Australia - Football", "Australia - Football", "Australia - Rugby",
    "Australia - Rugby"
  ),
  stringsAsFactors = FALSE
)

fig <- plot_ly(d, ids = ~ids, labels = ~labels, parents = ~parents, type = 'sunburst')

fig

Treemap

fig <- plot_ly(
  type="treemap",
  labels=c("Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"),
  parents=c("", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve")
)
fig

labels = c("Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura")
parents = c("", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve")

fig <- plot_ly(
  type='treemap',
  labels=labels,
  parents=parents,
  values= c(10, 14, 12, 10, 2, 6, 6, 1, 4),
  textinfo="label+value+percent parent+percent entry+percent root",
  domain=list(column=0))

fig <- fig %>% add_trace(
  type='treemap',
  branchvalues="total",
  labels=labels,
  parents=parents,
  values=c(65, 14, 12, 10, 2, 6, 6, 1, 4),
  textinfo="label+value+percent parent+percent entry",
  outsidetextfont=list(size=20, color= "darkblue"),
  marker=list(line= list(width=2)),
  pathbar=list(visible= FALSE),
  domain=list(column=1))

fig <- fig %>% layout(
  grid=list(columns=2, rows=1),
  margin=list(l=0, r=0, b=0, t=0))

fig

Diagrama Sankey

fig <- plot_ly(
    type = "sankey",
    orientation = "h",

    node = list(
      label = c("A1", "A2", "B1", "B2", "C1", "C2"),
      color = c("blue", "blue", "blue", "blue", "blue", "blue"),
      pad = 15,
      thickness = 20,
      line = list(
        color = "black",
        width = 0.5
      )
    ),

    link = list(
      source = c(0,1,0,2,3,3),
      target = c(2,3,3,4,4,5),
      value =  c(8,4,2,8,4,2)
    )
  )
fig <- fig %>% layout(
    title = "Basic Sankey Diagram",
    font = list(
      size = 10
    )
)

fig

Un Sankey un poco más laborioso

library(plotly)
library(rjson)

json_file <- "https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

fig <- plot_ly(
    type = "sankey",
    domain = list(
      x =  c(0,1),
      y =  c(0,1)
    ),
    orientation = "h",
    valueformat = ".0f",
    valuesuffix = "TWh",

    node = list(
      label = json_data$data[[1]]$node$label,
      color = json_data$data[[1]]$node$color,
      pad = 15,
      thickness = 15,
      line = list(
        color = "black",
        width = 0.5
      )
    ),

    link = list(
      source = json_data$data[[1]]$link$source,
      target = json_data$data[[1]]$link$target,
      value =  json_data$data[[1]]$link$value,
      label =  json_data$data[[1]]$link$label
    )
  ) 
fig <- fig %>% layout(
    title = "Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a href='https://bost.ocks.org/mike/sankey/'>Mike Bostock</a>",
    font = list(
      size = 10
    ),
    xaxis = list(showgrid = F, zeroline = F),
    yaxis = list(showgrid = F, zeroline = F)
)

fig

dygraph

sunburstR

highcharter

REF

Véase los siguientes sitios de referencia

lattice

https://www.statmethods.net/advgraphs/trellis.html http://www.sthda.com/english/wiki/lattice-graphs http://www.sthda.com/english/wiki/lattice-graphs https://www.rdocumentation.org/packages/lattice/versions/0.20-41 https://www.dummies.com/programming/r/how-to-create-a-lattice-plot-in-r/ https://data-flair.training/blogs/r-lattice-package/ https://stat.ethz.ch/R-manual/R-devel/library/lattice/html/Lattice.html http://www.datavis.ca/courses/RGraphics/R-Graphics3.pdf

ggplot2

https://www.r-graph-gallery.com/ggplot2-package.html http://r-statistics.co/ggplot2-Tutorial-With-R.html https://ggplot2.tidyverse.org/ https://www.statmethods.net/advgraphs/ggplot2.html https://rpubs.com/daniballari/ggplot http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html https://www.rdocumentation.org/packages/ggplot2/versions/3.3.2 https://swcarpentry.github.io/r-novice-gapminder-es/08-plot-ggplot2/ https://datacarpentry.org/R-ecology-lesson/04-visualization-ggplot2.html

ggchart

https://github.com/thomas-neitmann/ggcharts https://www.rdocumentation.org/packages/ggcharts/versions/0.2.1 https://rdrr.io/cran/ggcharts/ https://tereom.github.io/tutoriales/R_intro_visualizacion.html

Plotly

https://plotly.com/r/ https://plotly-r.com/ https://www.rdocumentation.org/packages/plotly/versions/4.9.2.1 https://www.neonscience.org/plotly https://www.it-swarm.dev/es/r-plotly/ https://github.com/ropensci/plotly https://rdrr.io/cran/plotly/man/ggplotly.html https://www.htmlwidgets.org/showcase_plotly.html https://www.r-bloggers.com/introduction-to-interactive-graphics-in-r-with-plotly/

dygraph

https://rstudio.github.io/dygraphs/ https://www.htmlwidgets.org/showcase_dygraphs.html https://github.com/rstudio/dygraphs https://blog.rstudio.com/2015/04/14/interactive-time-series-with-dygraphs/ https://rdrr.io/cran/dygraphs/man/dySeries.html https://rpubs.com/Da_As_Kumi/561455 https://bookdown.org/kochiuyu/Technical-Analysis-with-R/dygraphs-package.html http://www.imsbio.co.jp/RGM/R_function_list?package=dygraphs&init=true https://www.r-bloggers.com/how-to-plot-with-dygraphs-exercises/

sunburstR

http://rstudio-pubs-static.s3.amazonaws.com/495237_5336f47cb4df483f8f2780cd92066449.html https://www.data-to-viz.com/graph/sunburst.html https://github.com/timelyportfolio/sunburstR https://www.rdocumentation.org/packages/sunburstR/versions/2.1.3/topics/sunburst https://rdrr.io/cran/sunburstR/src/R/sunburst.R

highcharter

https://rstudio-pubs-static.s3.amazonaws.com/320413_6ab300527e8548b1a3cbd0d4c6200fcc.html https://www.highcharts.com/blog/tutorials/highcharts-for-r-users/ https://towardsdatascience.com/exploring-highcharts-in-r-f754143efda7 https://github.com/jbkunst/highcharter https://www.htmlwidgets.org/showcase_highcharts.html https://rdrr.io/cran/highcharter/man/hc_legend.html https://rpubs.com/jbkunst/highcharter http://uribo.github.io/rpkg_showcase/graphics/highcharter.html